Self-Modeling Based Diagnosis of Software-Defined Networks

Networks built using SDN (Software-Defined Networks) and NFV (Network Functions Virtualization) approaches are expected to face several challenges such as scalability, robustness and resiliency. In this paper, we propose a self-modeling based diagnosis to enable resilient networks in the context of SDN and NFV. We focus on solving two major problems: On the one hand, we lack today of a model or template that describes the managed elements in the context of SDN and NFV. On the other hand, the highly dynamic networks enabled by the softwarisation require the generation at runtime of a diagnosis model from which the root causes can be identified. In this paper, we propose finer granular templates that do not only model network nodes but also their sub-components for a more detailed diagnosis suitable in the SDN and NFV context. In addition, we specify and validate a self-modeling based diagnosis using Bayesian Networks. This approach differs from the state of the art in the discovery of network and service dependencies at run-time and the building of the diagnosis model of any SDN infrastructure using our templates

Self-Modeling based Diagnosis of Services over Programmable Networks

International audienceIn this paper, we propose a multi-layer self-diagnosis framework for networking services within SDN and NFV environments. The framework encompasses three main contributions: 1) the definition of multi-layered templates to identify what to supervise while taking into account the physical, logical, virtual and service layers. These templates are also finer-granular, extendable and machine-readable; 2) a self-modeling module that takes as input these templates, instantiates them and generates on-the-fly the diagnosis model that includes the physical, logical, and the virtual dependencies of networking services; 3) a service-aware root-cause analysis module that takes into account the networking services' views and their underlying network resources observations within the aforementioned layers. We also present extensive simulations to prove the fully automated, finer granularity and reduced uncertainty of the root cause of networking services failures and their underlying network resources

Optimization of fault diagnosis based on the combination of Bayesian Networks and case Based Reasoning

International audienceFault diagnosis is one of the most important tasks in fault management. The main objective of the fault management system is to detect and localize failures as soon as they occur to minimize their effects on the network performance and therefore on the service quality perceived by users. In this paper, we present a new hybrid approach that combines Bayesian Networks and Case-Based Reasoning to overcome the usual limits of fault diagnosis techniques and reduce human intervention in this process. The proposed mechanism allows identifying the root cause failure with a finer precision and high reliability while reducing the process computation time and taking into account the network dynamicity

Optimization of fault diagnosis based on the combination of Bayesian Networks and case Based Reasoning",

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